By Topic

Estimation of dynamic neural activity using a Kalman filter approach based on physiological models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Giraldo, E. ; Fac. of Electr. & Electron. Eng., Phys. & Comput. Sci., Technol. Univ. of Pereira, Pereira, Colombia ; den Dekker, A.J. ; Castellanos-Dominguez, G.

This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.

Published in:

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE

Date of Conference:

Aug. 31 2010-Sept. 4 2010